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Part I. Syntax

List comprehension

Erlang:

Lst = [1,2,3,4],
[X + 1 || X <- Lst],
lists:map(fun(X) -> X + 1 end, Lst)

Scala:

val lst = List(1,2,3,4) 
for (x <- lst) yield x + 1
lst.map{x => x + 1}
lst.map{_ + 1} // or place holder

Pattern match

Erlang:

case X of
   {A, B} when is_integer(A), A > 1 -> ok
   _ -> error
end

Scala:

x match {
   case (a:Int, b:_) if a > 1 => OK // can match type
   case _ => ERROR
}

List, Tuple, Array, Map, Binary, Bit

Erlang:

Lst = [1, 2, 3]
[0 | Lst]
{1, 2, 3}
<<1, 2, “abc”>>
%% no Array, Map syntax

Scala:

val lst = List(1, 2, 3)
0 :: lst
(1, 2, 3)
Array(1, 2, 3) 
Map(“a” -> 1, “b” -> 2)
// no Binary, Bit syntax

Process, Actor

Erlang:

the_actor(X) -> 
   receive 
      ok -> io:format(“~p~n”, [X])
      I -> the_actor(X + I) %% needs to explicitly continue loop
   end.
P = spawn(mymodule, the_actor, [0])
P ! 1
P ! ok

Scala:

class TheActor(x:Int) extends Actor { 
   def act = loop {
      react {
         case “ok” => println(x); exit // needs to explicitly exit loop
         case i:Int => x + i
      }
   }
}
val a = new TheActor(0)
a ! 1
a ! “ok”

Part II. Processes vs Actors

Something I

Erlang:

• Lightweight processes
• You can always (almostly) create a new process for each new comer
• Scheduler treats all processes fairly
• Share nothing between processes
• Lightweight context switch between processes
• IO has been carefully delegated to independent processes

Scala:

• Active actor is delegated to JVM thread, actor /= thread
• You can create a new actor for each new comer
• But the amount of real workers (threads) is dynamically adjusted according to the processing time
• The later comers may be in wait list for further processing until a spare thread is available
• Share nothing or share something upon you decision
• Heavy context switch between working threads
• IO block is still pain unless good NIO framework (Grizzly?)

Something II

Erlang:

• Try to service each one concurrently
• But may loss service quality when the work is heavy, may time out (out of service)
• Ideal for context switch cost is comparable to processing cost
• Ideal for small message processing in soft real-time
• Bad for massive data processing, and cpu-heavy work

Scala:

• Try to service limited number of customers best first
• If can not service all, the later comers will be put in waiting list and may time out (out of service)
• It’s difficult for soft real-time on all coming concurrent customers
• Ideal for context switch cost is far less than processing cost (context switch time is in ns on modern JVM)
• When will there be perfect NIO + Actor library?